Digital subtraction angiography has been known as a recording technique in medical imaging for a long time and is conventionally abbreviated to DSA. In digital subtraction angiography a recording region of a patient is recorded at least twice: once without the effect of contrast medium (mask image), and another time with the effect of contrast medium (fill image). If the mask image is subtracted from the fill image, a result image is produced, which may show only the contrast medium. In this way, information, for example, on the circulation may be obtained, with it also being known to collate temporal information by finally partially or completely capturing the flow of contrast medium through the recording region by way of a plurality of fill images. Digital subtraction angiography is conceivable here in both two and three dimensions, wherein the following statements primarily refer to 2D DSA.
One frequent problem with digital subtraction angiography, in particular 2D DSA, are artifacts in the result images, which are caused by patient movements between the acquisition of the mask image and of the fill image. If a mask image and a fill image show the recording region in different movement states, the anatomy to be subtracted therefrom dose not exactly match, so that residues thereof remain as edges or other artifacts in the result image. In particular, independently of the recording region and the type of movement, complex movement patterns may occur, which may be composed of the overlaying of a plurality of organs or other anatomical structures that move in different ways.
In order to compensate such movement artifacts, it has been proposed in the prior art to register a mask image by way of elastic registering, in particular therefore a deformable movement field, on the respective fill images. In an approach of this kind, as a rule, the movements of overlaid organs having different movement patterns are not adequately considered and therefore compensated, however.
In an alternative approach, a plurality of mask images is recorded in different movement states of the recording region and is manually allocated to optimally similar fill images. The manual allocation is very time-consuming, however, and it is not possible to provide that suitable mask images have been recorded for the movement state of each fill image.